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This tutorial is not complete.<br>
###############################################################################
# #
# Copyright (C) 2013-2014 Troels E. Linnet #
# #
# This file is part of the program relax (http://www.nmr-relax.com). #
# #
# This program is free software: you can redistribute it and/or modify #
# it under the terms of the GNU General Public License as published by #
# the Free Software Foundation, either version 3 of the License, or #
# (at your option) any later version. #
# #
# This program is distributed in the hope that it will be useful, #
# but WITHOUT ANY WARRANTY; without even the implied warranty of #
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the #
# GNU General Public License for more details. #
# #
# You should have received a copy of the GNU General Public License #
# along with this program. If not, see <http://www.gnu.org/licenses/>. #
# #
###############################################################################
Using the {{caution}} template.
{{caution|This tutorial is incomplete.}}
= Intro =
This tutorial is based on the analysis of R1rho data, analysed in a master thesis.
# a high number should be provided. Later the high quality R2eff values will be read for subsequent model analyses.
if not hasattr(ds, 'exp_mc_sim_num'):
ds.exp_mc_sim_num = 102000
# The result directory.
from auto_analyses.relax_disp import Relax_disp
from data_store import Relax_data_store; ds = Relax_data_store()
from specific_analyses.relax_disp.variables import MODEL_R2EFF, MODEL_NOREX_R1RHO_FIT_R1MODEL_NOREX_R1RHO, MODEL_DPL94_FIT_R1MODEL_DPL94, MODEL_TP02_FIT_R1MODEL_TP02, MODEL_TAP03_FIT_R1MODEL_TAP03, MODEL_MP05_FIT_R1MODEL_MP05
#########################################
# The models to analyse.
if not hasattr(ds, 'models'):
#ds.models = [MODEL_NOREX_R1RHO_FIT_R1MODEL_NOREX_R1RHO, MODEL_DPL94_FIT_R1MODEL_MP05, MODEL_TP02_FIT_R1MODEL_DPL94, MODEL_TAP03_FIT_R1MODEL_TP02, MODEL_MP05_FIT_R1MODEL_TAP03] ds.models = [MODEL_NOREX_R1RHO_FIT_R1MODEL_NOREX_R1RHO, MODEL_DPL94_FIT_R1MODEL_DPL94]
# The number of increments per parameter, to split up the search interval in grid search.
# Load the previous results into the base pipe.
results.read(file='results', dir=ds.pre_run_dir)
# If R1 is not measured, then do R1 fitting.
r1_fit=True
# Run the analysis.
Relax_disp(pipe_name=ds.pipe_name, pipe_bundle=ds.pipe_bundle, results_dir=ds.results_dir, models=ds.models, grid_inc=ds.grid_inc, mc_sim_num=ds.mc_sim_num, modsel=ds.modsel, r1_fit=r1_fit)
</source>
from pipe_control.mol_res_spin import generate_spin_string, return_spin, spin_loop
from specific_analyses.relax_disp.data import generate_r20_key, loop_exp_frq
from specific_analyses.relax_disp.variables import MODEL_R2EFF, MODEL_NOREX_R1RHO_FIT_R1MODEL_NOREX_R1RHO, MODEL_DPL94_FIT_R1MODEL_DPL94, MODEL_TP02_FIT_R1MODEL_TP02, MODEL_TAP03_FIT_R1MODEL_TAP03, MODEL_MP05_FIT_R1MODEL_MP05
#########################################
# Define models which have been analysed.
#MODELS = [MODEL_NOREX_R1RHO_FIT_R1MODEL_NOREX_R1RHO MODEL_MP05, MODEL_DPL94_FIT_R1MODEL_DPL94, MODEL_TP02_FIT_R1MODEL_TP02, MODEL_TAP03_FIT_R1MODEL_TAP03, MODEL_MP05_FIT_R1MODEL_MP05]MODELS = [MODEL_NOREX_R1RHO_FIT_R1MODEL_NOREX_R1RHO, MODEL_DPL94_FIT_R1MODEL_DPL94]
# Print results for each model.
file: '''5_clustered_analyses.py'''.
<source lang="Python">
# Python module imports.
from os import getcwd, sep
from data_store import Relax_data_store; ds = Relax_data_store()
from pipe_control.mol_res_spin import spin_loop
from specific_analyses.relax_disp.variables import MODEL_R2EFF, MODEL_NOREX_R1RHO_FIT_R1MODEL_NOREX_R1RHO, MODEL_DPL94_FIT_R1MODEL_DPL94, MODEL_TP02_FIT_R1MODEL_TP02, MODEL_TAP03_FIT_R1MODEL_TAP03, MODEL_MP05_FIT_R1MODEL_MP05
#########################################
# The models to analyse.
if not hasattr(ds, 'models'):
#ds.models = [MODEL_NOREX_R1RHO_FIT_R1MODEL_NOREX_R1RHO, MODEL_DPL94_FIT_R1MODEL_DPL94, MODEL_TP02_FIT_R1MODEL_TP02, MODEL_TAP03_FIT_R1MODEL_TAP03, MODEL_MP05_FIT_R1MODEL_MP05] ds.models = [MODEL_DPL94_FIT_R1MODEL_DPL94]
# The number of increments per parameter, to split up the search interval in grid search.
